Deep Learning -Based Diabetic Retinopathy Detection and Severity Classification using CNN ResNET

• Author(s): Yashashwini S.
• Paper ID: 1719788
• Page: 1062-1067
• Published Date: 13-07-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 10 Issue 1 July-2026
• DOI: https://doi.org/10.64388/IREV10I1-1719788

Abstract

Diabetic Retinopathy (DR) is an ocular complication associated with diabetes and remains one of the primary contributors to vision impairment among the working-age population. The condition arises when prolonged high blood glucose levels damage the retinal blood vessels, causing them to dilate, leak fluids, or obstruct normal circulation. As the disease advances, it can lead to severe visual decline or even complete blindness. The central aim of this research is to enable early detection of DR by applying machine learning approaches and to classify retinal images into five distinct stages: No DR, Mild DR, Moderate DR, Severe DR, and Proliferative DR. Effective categorization of such medical images is facilitated through the use of a Convolutional Neural Network (CNN) architecture.

Keywords

Diabetic Retinopathy, Fundus Imaging, Artificial Intelligence, Convolutional Neural Network, Proliferative DR, Non-Proliferative DR, Visual Impairment.

Citations

IRE Journals:
Yashashwini S. "Deep Learning -Based Diabetic Retinopathy Detection and Severity Classification using CNN ResNET" Iconic Research And Engineering Journals Volume 10 Issue 1 2026 Page 1062-1067 https://doi.org/10.64388/IREV10I1-1719788

IEEE:
Yashashwini S. "Deep Learning -Based Diabetic Retinopathy Detection and Severity Classification using CNN ResNET" Iconic Research And Engineering Journals, vol. 10, no. 1, Jul. 2026, doi: https://doi.org/10.64388/IREV10I1-1719788

APA:
Yashashwini S. (2026). Deep Learning -Based Diabetic Retinopathy Detection and Severity Classification using CNN ResNET. Iconic Research And Engineering Journals, 10(1). doi: https://doi.org/10.64388/IREV10I1-1719788

MLA:
Yashashwini S. "Deep Learning -Based Diabetic Retinopathy Detection and Severity Classification using CNN ResNET" Iconic Research And Engineering Journals, vol. 10, no. 1, Jul. 2026. Crossref, https://doi.org/10.64388/IREV10I1-1719788

BibTeX

@article{1719788,
author = {Yashashwini S.},
title = {Deep Learning -Based Diabetic Retinopathy Detection and Severity Classification using CNN ResNET},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {1},
pages = {1062-1067},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719788.pdf},
abstract = {Diabetic Retinopathy (DR) is an ocular complication associated with diabetes and remains one of the primary contributors to vision impairment among the working-age population. The condition arises when prolonged high blood glucose levels damage the retinal blood vessels, causing them to dilate, leak fluids, or obstruct normal circulation. As the disease advances, it can lead to severe visual decline or even complete blindness. The central aim of this research is to enable early detection of DR by applying machine learning approaches and to classify retinal images into five distinct stages: No DR, Mild DR, Moderate DR, Severe DR, and Proliferative DR. Effective categorization of such medical images is facilitated through the use of a Convolutional Neural Network (CNN) architecture.},
keywords = {Diabetic Retinopathy, Fundus Imaging, Artificial Intelligence, Convolutional Neural Network, Proliferative DR, Non-Proliferative DR, Visual Impairment.},
month = {July}
}